In order to ensure desirable educational outcomes for students, a great deal of thought and planning must occur with regard to how coursework is designed and executed. While students themselves may only think in terms of passing their current course, educational system designers, administrators, and others must also develop programs within institutions that require students to engage in learning that helps them to reach particular objectives.
Broadly, successful educational system designs tend to utilize a paradigm of starting with a broad set of institutional objectives. These broad objectives are then met by implementing programs formulated to meet those objectives. Programs, in turn, comprise discrete courses or experiences with actual content or experiences for students that build toward both programmatic and institutional level objectives.
As such, programs within an institution are often the main focus of institutional and educational planning. Successful programs are likely organized in a manner that ensures high-level institutional objectives by ensuring everything from an atomized content chapter from a text book all the way to graduation, maps from the atomic level, to course objectives and/or competencies, then to program objectives, and finally to institutional objectives. However, the interrelationships among these points are far more complex than a simple “A” leads to “B” which leads to “C” system. Rather, a particular data point often maps to points above, below, and parallel within the data structure. In other scenarios, only collections of data points at one level are capable of successfully mapping to a higher-level objective so it becomes imperative to understand all of the interrelationships in order to design a successful educational system data structure.
As such, tightly integrated educational system data structures result in extremely complex interrelationships among hundreds or even thousands of discrete learning objectives at just a program level. For large institutions like traditional universities, millions of data points must be planned, implemented, tracked, and maintained in order to ensure students obtain the requisite content and experiences in order to meet institutional objectives and success metrics.
As one hypothetical example, many large State Universities in the United States have 15-20 degree-granting colleges comprising 200, or more, different academic programs. At the University level, numerous broad goals and outcomes are likely to be identified as representing skills, behaviors, or other broad characteristics the University has deemed necessary for students to obtain by the end of a given program. Likewise, each of the 200 or so programs will have broad goals and objectives that are designed to feed into the University goals and outcomes. Next, each of those 200 or so academic programs will often comprise as many as 50 individual courses. Within each individual course, perhaps 5-10 specific course outcomes are identified. Finally, in order to achieve those 5-10 course outcomes, each specific outcome is atomized into numerous individual student activities, experiences, learning objectives, or other learning items. Thus, for an organization with a structure similar to this hypothetical State University, it is entirely conceivable that tens of millions (if not hundreds of millions) of interrelationships must be identified and their relationships tracked and validated, to ensure that students who complete programs have been exposed to the content and objectives sufficient to master learning objectives, competencies, courses, programs, degrees, and University outcomes. Additionally, as objectives change, the interrelationships must also be changed in an efficient manner.
Similar instructional design use cases also exist within the Kindergarten through 12th grade (K-12) educational system. While the demands within an individual school may be less than a traditional University, the data interrelationships are still vastly more complex than can effectively been managed using current systems. Further, when multiple schools need to be managed at district, State, or even Federal levels, management and validation of educational systems becomes extremely complex.
Further, while traditional educational systems will be used throughout this specification as being obvious benefactors of the methods and systems described herein, it should also be recognized that there is applicability within corporate training and education environments, as well.
Regardless of the level of educational system or the type of corporate training environment that is utilizing the embodiments described herein, there is likely a need to uphold standards, accreditations, and certifications. The process for proving certification, accreditation, and standards mapping is extremely difficult and complex to provide. Businesses and institutions depend on meeting these requirements. Thus, it should be appreciated that a need exists for providing efficient methods and systems to create and maintain the vast datasets and interrelationships described above.
Current systems for managing such large datasets of relationships are cumbersome and unreliable. Commonly, due to the vast array of programs and courses within a large institution, silos develop. This often results in courseware development and revisioning occurring at a course or even learning objective level without corresponding changes being reflected to, or reflective of, higher level objectives or requirements. Similarly, it is also common for high-level institutional or programmatic objectives or requirements to change requiring some system or method for propagating, or at least validating, the presence of those changes all the way down to any particular atomized learning objective or experience.
When revisions are necessary, additional infrastructure or avenues are needed for stakeholders to discuss changes that are needed. Commonly, such information simply resides in the memories of the individual stakeholders, or perhaps within disparate data stores (e.g., email archives.) As such, is it also desirable to include within a course structure dataset a methodology of capturing institutional knowledge and dataset relational history.
Commonly, vast collections of static tracking methods are utilized in an attempt to track the necessary relationships. However, traditional modes of organization such as spreadsheets are simply inadequate to efficiently and accurately track such large numbers of relationships, and that is to say nothing about managing long-term historical information regarding information such as decisions made in prior course versions, historically different institutional directives, or due to changes in accreditation or the like. Commonly, such information is simply never formally recorded, and even where it is, there is often little to no context linking it to related dataset components in a manner that allows it to be leveraged at a later time by perhaps a different set of educational system designers or institution administrators.
Even in scenarios where such enormous data sets can be reliably tracked, the ability for educators to interact with the data in a meaningful way is difficult at best because flat file databases, tracking sheets, and other such means simply do not provide enough transparency into the data to be helpful. Additionally, the configuration, maintenance, and use of such prior art systems requires technically adept users who are also familiar with the underlying data structures. And even then, such users are unlikely to discover some types of relationships.
Computer systems and related technology affect many aspects of society. Indeed, the computer system's ability to process information has transformed the way we live and work. Computer systems now commonly perform a host of tasks (e.g., word processing, scheduling, accounting, etc.) that prior to the advent of the computer system were performed manually. More recently, computer systems have been coupled to one another and to other electronic devices to form both wired and wireless computer networks over which the computer systems and other electronic devices can transfer electronic data. As such, the performance of many computing tasks has become distributed across a number of different computer systems and/or a number of different computer environments.
Computer systems have also enabled the educational dataset management structures that can handle the enormous datasets and interrelationships inherent in such structures, as discussed herein. Additionally, user interfaces are now capable of presenting such relationships in unified ways and in a consistent interface. Additionally, computing systems have enabled additional capabilities for managing educational planning datasets that were simply impossible prior to the existence of dedicated hardware and software systems such as those presented herein.
The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one exemplary technology area where some embodiments described herein may be practiced.